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Subject Matter Expert Evaluation of Multi-Flight Common Route Advisories
Author(s) -
Karl Bilimoria,
Miwa Hayashi,
Kapil Sheth
Publication year - 2017
Publication title -
14th aiaa aviation technology, integration, and operations conference
Language(s) - English
Resource type - Conference proceedings
DOI - 10.2514/6.2017-3426
Subject(s) - computer science , subject matter , subject matter expert , subject (documents) , artificial intelligence , expert system , world wide web , psychology , pedagogy , curriculum
Flights often experience large delays when they are routed around weather. Multi-flight common route advisories provide time-saving re-routes for groups of flights whose current weather-avoidance routes have become outdated because the weather has dissipated and/or moved away. A laboratory evaluation of these advisories was conducted by five subject matter experts having extensive experience in traffic flow management operations. These experts provided a total of 200 data sets in the airspace of Fort Worth Center and Houston Center. The tool identifies time-saving route changes taking into account flight plans, wind fields, and the spatial-temporal evolution of predicted convective weather. The tool is not designed to account for complex operational factors such as non-standard sector traversal, and hence a relatively low percentage of advisories initially generated by the tool were rated as acceptable (55% for Fort Worth Center and 22% for Houston Center). However, a high percentage of advisories were rated as acceptable (86% for Fort Worth Center and 75% for Houston Center) after the subject matter experts used the tool’s user interface to make route modifications that accounted for relevant operational factors. The workload associated with using the tool was rated as low for 80% of Fort Worth Center advisories and 56% of Houston Center advisories. On average, Houston Center advisories provided larger time savings but were more complex, hence they were more challenging to evaluate and received less favorable ratings. The results of this evaluation make a good case for human-automation teaming to design weather re-routes for delay recovery.

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